Tailored Parenting Plans of Young Adults With Sickle Cell Disease or Sickle Cell Trait
Bibliographic record
Abstract
Our study purpose was to evaluate the variation and accuracy of tailored parenting plans individually generated as a supplement to reproductive health education on the genetic inheritance of sickle cell disease or sickle cell trait. We present a secondary data analysis of experimental group data from a randomized controlled trial. Participants completed the valid and reliable Internet-based Sickle Cell Reproductive Health Knowledge Parenting Intent Questionnaire. We created a computerized algorithm that used participants' responses to generate tailored parenting plans based on their parenting preferences and partner's sickle cell status. Thirty-one different parenting plans were generated to meet the variety in the participants' preferences. The most frequently generated plan was for participants with sickle cell disease who had a partner with hemoglobin AA, who wanted to be a parent, was not likely to be pregnant, and wanted their child to be sickle cell disease free. More than half of the participants required alteration in their reproductive behavior to achieve their parenting goals. Findings provide insight into the variety and accuracy of computer algorithm-generated parenting plans, which could further guide refinement of the algorithm to produce patient-centered, tailored parenting plans supplemental to Internet-based genetic inheritance education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".